Automating table-based groundtruth generation

ABSTRACT

A method, system and computer-usable medium are disclosed for automating the generation of table-based groundtruth, comprising: receiving a document comprising unstructured text and a table; generating questions by applying a template the contents of the table; performing QA pair generation operations on the table to generate QA pairs, each QA pair comprising a question generated by applying the template; and, assigning a score to each QA pair, the score providing an indicator of user interest to each QA pair, the score being based on a score generation methodology using the unstructured text and the table.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. More particularly, it relates to a method, system andcomputer-usable medium for improved coverage of tables in a corpus whengenerating question-answer (QA) pairs used to train a QA system.

Description of the Related Art

Current implementations of question-answer (QA) systems are primarilyoriented towards processing unstructured text within a corpus to provideanswers to a given question. The veracity of such answers is oftendependent upon the groundtruth (i.e., questions with identified correctanswers) used to train the QA system. While such approaches can beeffective in providing qualitative answers, they typically are lesseffective in providing quantitative answers (e.g., numerical,categorical, percentage, date, time, etc.) unless such information isalready present within the unstructured text.

More often, quantitative information is instead provided as structureddata in a tabular format, such as a table or a set of bulleted items.The veracity of answers to questions on such structured content is alsooften dependent upon the groundtruth (i.e., question with identifiedcorrect answers) used to train the QA system.

However, known approaches for generating training QA pairs based onstructured data have limitations. As an example, generating all possiblequestions from repeated-structure content, such as that commonly foundin a table, may result in a plethora of QA pairs that may not be ofsignificant interest to a user. As a result, providing groundtruth(i.e., questions with identified correct answers) for data stored intables can prove challenging, and by extension, negatively affect QAsystem training.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed for automatingthe generation of table-based groundtruth, comprising: receiving adocument comprising unstructured text and a table; generating questionsby applying a template the contents of the table; performing QA pairgeneration operations on the table to generate QA pairs, each QA paircomprising a question generated by applying the template; and, assigninga score to each QA pair, the score providing an indicator of userinterest to each QA pair, the score being based on a score generationmethodology using the unstructured text and the table.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of an information handling systemcapable of performing computing operations;

FIG. 3 is a simplified block diagram of a table-based groundtruthgeneration system;

FIG. 4 is an exemplary table used in automating the generation oftable-based groundtruths; and

FIGS. 5a through 5c are a generalized flowchart of the performance oftable-based groundtruth generation operations.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for improvedcoverage of tables in a corpus when generating question-answer (QA)pairs used to train a QA system. The present invention may be a system,a method, and/or a computer program product. In addition, selectedaspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.), or an embodimentcombining software and/or hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the present invention may take the form of computer programproduct embodied in a computer readable storage medium, or media, havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a Public SwitchedCircuit Network (PSTN), a packet-based network, a personal area network(PAN), a local area network (LAN), a wide area network (WAN), a wirelessnetwork, or any suitable combination thereof. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine-dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language, Hypertext Precursor (PHP), or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer, or entirely on the remote computer or server orcluster of servers. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a sub-system, module, segment,or portion of instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system 100 and a question prioritization system 110connected to a computer network 140. The QA system 100 includes aknowledge manager 104 that is connected to a knowledge base 106 andconfigured to provide question/answer (QA) generation functionality forone or more content creators and/or users 130 who submit across thenetwork 140 to the QA system 100. To assist with efficient sorting andpresentation of questions to the QA system 100, the questionprioritization system 110 may be connected to the computer network 140to receive user questions, and may include a plurality of subsystemswhich interact with cognitive systems, like the QA system 100, toprioritize questions or requests being submitted to the QA system 100.

The Named Entity subsystem 112 receives and processes each question 111by using natural language processing (NLP) to analyze each question andextract question topic information contained in the question, such asnamed entities, phrases, urgent terms, and/or other specified termswhich are stored in one or more domain entity dictionaries 113. Byleveraging a plurality of pluggable domain dictionaries 113 relating todifferent domains or areas (e.g., travel, healthcare, electronics, gameshows, financial services, etc.), the domain dictionary 113 enablescritical and urgent words (e.g., “threat level”) from different domains(e.g., “travel”) to be identified in each question based on theirpresence in the domain dictionary 113. To this end, the Named Entitysubsystem 112 may use an NLP routine to identify the question topicinformation in each question. As used herein, “NLP” broadly refers tothe field of computer science, artificial intelligence, and linguisticsconcerned with the interactions between computers and human (natural)languages. In this context, NLP is related to the area of human-computerinteraction and natural language understanding by computer systems thatenable computer systems to derive meaning from human or natural languageinput. For example, NLP can be used to derive meaning from ahuman-oriented question such as, “What is tallest mountain in NorthAmerica?” and to identify specified terms, such as named entities,phrases, or urgent terms contained in the question. The processidentifies key terms and attributes in the question and compares theidentified terms to the stored terms in the domain dictionary 113.

The Question Priority Manager subsystem 114 performs additionalprocessing on each question to extract question context information115A. In addition, or in the alternative, the Question Priority Managersubsystem 114 may also extract server performance information 115B forthe question prioritization system 110 and/or QA system 100. In selectedembodiments, the extracted question context information 115A may includedata that identifies the user context and location when the question wassubmitted or received. For example, the extracted question contextinformation 115A may include data that identifies the user who submittedthe question (e.g., through login credentials), the device or computerwhich sent the question, the channel over which the question wassubmitted, or any combination thereof. Other examples may include thelocation of the user or device that sent the question, any specialinterest location indicator (e.g., hospital, public-safety answeringpoint, etc.), other context-related data for the question, or anycombination thereof. In certain embodiments, the location information isdetermined through the use of a Geographical Positioning System (GPS)satellite 168. In these embodiments, a handheld computer or mobiletelephone 150, or other device, uses signals transmitted by the GPSsatellite 168 to generate location information, which in turn isprovided via the computer network 140 to the Question Priority Managersubsystem 114 for processing.

In various embodiments, the source for the extracted context information115A may be a data source 166 accessed through the computer network 140.Examples of a data source 166 include systems that provide telemetryinformation, such as medical information collected from medicalequipment used to monitor a patient's health, environment informationcollected from a facilities management system, or traffic flowinformation collected from a transportation monitoring system. Incertain embodiments, the data source 166 may be a storage area network(SAN) or other network-based repositories of data.

In various embodiments, the data source 166 may provide data directly orindirectly collected from “big data” sources. In general, big datarefers to a collection of datasets so large and complex that traditionaldatabase management tools and data processing approaches are inadequate.These datasets can originate from a wide variety of sources, includingcomputer systems (e.g., 156, 158, 162), mobile devices (e.g., 150, 152,154), financial transactions, streaming media, social media, as well assystems (e.g., 166) commonly associated with a wide variety offacilities and infrastructure (e.g., buildings, factories,transportation systems, power grids, pipelines, etc.). Big data, whichis typically a combination of structured, unstructured, andsemi-structured data poses multiple challenges, including its capture,curation, storage, transfer, search, querying, sharing, analysis andvisualization.

The Question Priority Manager subsystem 114 may also determine orextract selected server performance data 115B for the processing of eachquestion. In certain embodiments, the server performance information115B may include operational metric data relating to the availableprocessing resources at the question prioritization system 110 and/or QAsystem 100, such as operational or run-time data, CPU utilization data,available disk space data, bandwidth utilization data, and so forth. Aspart of the extracted information 115A/B, the Question Priority Managersubsystem 114 may identify the Service Level Agreement (SLA) or Qualityof Service (QoS) processing requirements that apply to the questionbeing analyzed, the history of analysis and feedback for the question orsubmitting user, and the like. Using the question topic information andextracted question context 115A and/or server performance information115B, the Question Priority Manager subsystem 114 is configured topopulate feature values for the Priority Assignment Model 116. Invarious embodiments, the Priority Assignment Model 116 provides amachine learning predictive model for generating target priority valuesfor the question, such as by using an artificial intelligence (AI)approaches known to those of skill in the art. In certain embodiments,the AI logic is used to determine and assign a question urgency value toeach question for purposes of prioritizing the response processing ofeach question by the QA system 100.

The Prioritization Manager subsystem 117 performs additional sort orrank processing to organize the received questions based on at least theassociated target priority values such that high priority questions areput to the front of a prioritized question queue 118 for output asprioritized questions 119. In the question queue 118 of thePrioritization Manager subsystem 117, the highest priority question isplaced at the front of the queue for delivery to the assigned QA system100. In selected embodiments, the prioritized questions 119 from thePrioritization Manager subsystem 117 that have a specified targetpriority value may be assigned to a particular pipeline (e.g., QA systempipeline 100A, 100B) in the QA system 100. As will be appreciated, thePrioritization Manager subsystem 117 may use the question queue 118 as amessage queue to provide an asynchronous communications protocol fordelivering prioritized questions 119 to the QA system 100. Consequently,the Prioritization Manager subsystem 117 and QA system 100 do not needto interact with a question queue 118 at the same time by storingprioritized questions in the question queue 118 until the QA system 100retrieves them. In this way, a wider asynchronous network supports thepassing of prioritized questions 119 as messages between different QAsystem pipelines 100A, 100B, connecting multiple applications andmultiple operating systems. Messages can also be passed from queue toqueue in order for a message to reach the ultimate desired recipient. Anexample of a commercial implementation of such messaging software isIBM's WebSphere MQ (previously MQ Series). In selected embodiments, theorganizational function of the Prioritization Manager subsystem 117 maybe configured to convert over-subscribing questions into asynchronousresponses, even if they were asked in a synchronized fashion.

The QA system 100 may include one or more QA system pipelines 100A,100B, each of which includes a computing device 104 comprising one ormore processors and one or more memories. The QA system pipelines 100A,100B may likewise include potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like. In various embodiments, thesecomputing device elements may be implemented to process questionsreceived over the network 140 from one or more content creator and/orusers 130 at computing devices (e.g., 150, 152, 154, 156, 158, 162). Incertain embodiments, the one or more content creator and/or users 130are connected over the network 140 for communication with each other andwith other devices or components via one or more wired and/or wirelessdata communication links, where each communication link may comprise oneor more of wires, routers, switches, transmitters, receivers, or thelike. In this networked arrangement, the QA system 100 and network 140may enable question/answer (QA) generation functionality for one or morecontent users 130. Other embodiments of QA system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

In each QA system pipeline 100A, 100B, a prioritized question 119 isreceived and prioritized for processing to generate an answer 120. Insequence, prioritized questions 119 are de-queued from the sharedquestion queue 118, from which they are de-queued by the pipelineinstances for processing in priority order rather than insertion order.In selected embodiments, the question queue 118 may be implemented basedon a “priority heap” data structure. During processing within a QAsystem pipeline (e.g., 100A, 100B), questions may be split into multiplesubtasks, which run concurrently. In various embodiments, a singlepipeline instance may process a number of questions concurrently, butonly a certain number of subtasks. In addition, each QA system pipeline100A, 100B may include a prioritized queue (not shown) to manage theprocessing order of these subtasks, with the top-level prioritycorresponding to the time that the corresponding question started (i.e.,earliest has highest priority). However, it will be appreciated thatsuch internal prioritization within each QA system pipeline 100A, 100Bmay be augmented by the external target priority values generated foreach question by the Question Priority Manager subsystem 114 to takeprecedence, or ranking priority, over the question start time. In thisway, more important or higher priority questions can “fast track”through a QA system pipeline 100A, 100B if it is busy withalready-running questions.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the question prioritization system 110, network140, a knowledge base or corpus of electronic documents 107 or otherdata, semantic data 108, content creators, and/or users 130, and otherpossible sources of input. In selected embodiments, some or all of theinputs to knowledge manager 104 may be routed through the network 140and/or the question prioritization system 110. The various computingdevices (e.g., 150, 152, 154, 156, 158, 162) on the network 140 mayinclude access points for content creators and/or users 130. Some of thecomputing devices may include devices for a database storing a corpus ofdata as the body of information used by the knowledge manager 104 togenerate answers to questions. The network 140 may include local networkconnections and remote connections in various embodiments, such thatknowledge manager 104 may operate in environments of any size, includinglocal (e.g., a LAN) and global (e.g., the Internet). Additionally,knowledge manager 104 serves as a front-end system that can makeavailable a variety of knowledge extracted from or represented indocuments, network-accessible sources and/or structured data sources. Inthis manner, some processes populate the knowledge manager, with theknowledge manager also including input interfaces to receive knowledgerequests and respond accordingly.

In one embodiment, a content creator 130 creates content (e.g., adocument) in a knowledge base 106 for use as part of a corpus of dataused in conjunction with knowledge manager 104. In selected embodiments,the knowledge base 106 may include any file, text, article, or source ofdata (e.g., scholarly articles, dictionary definitions, encyclopediareferences, and the like) for use by the knowledge manager 104. Contentusers 130 may access the knowledge manager 104 via a network connectionor an Internet connection to the network 140, and may input questions tothe knowledge manager 104 that may be answered by the content in thecorpus of data.

As further described below, when a process evaluates a given section ofa document for semantic content, the process can use a variety ofconventions to query it from the knowledge manager 104. One conventionis to send a well-formed question. As used herein, semantic contentbroadly refers to content based upon the relation between signifiers,such as words, phrases, signs, and symbols, and what they stand for,their denotation, or connotation. In other words, semantic content iscontent that interprets an expression, such as by using Natural Language(NL) Processing. In one embodiment, the process sends well-formedquestions (e.g., natural language questions, etc.) to the knowledgemanager 104. In various embodiments, the knowledge manager 104 mayinterpret the question and provide a response to the content usercontaining one or more answers to the question. In some embodiments, theknowledge manager 104 may provide a response to users in a ranked listof answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input prioritized question 119 and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis (e.g., comparisons), and generates a score.For example, certain reasoning algorithms may look at the matching ofterms and synonyms within the language of the input question and thefound portions of the corpus of data. Other reasoning algorithms maylook at temporal or spatial features in the language, while yet othersmay evaluate the source of the portion of the corpus of data andevaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 120 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson™ and How it Works” byRob High, IBM Redbooks, 2012.

Types of information processing systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 150 to large mainframe systems, such as mainframe computer158. Examples of handheld computer 150 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation processing systems include pen, or tablet, computer 152,laptop, or notebook, computer 154, personal computer system 156, server162, and mainframe computer 158.

As shown, the various information processing systems can be networkedtogether using computer network 140. Types of computer network 140 thatcan be used to interconnect the various information processing systemsinclude Personal Area Networks (PANs), Local Area Networks (LANs),Wireless Local Area Networks (WLANs), the Internet, the Public SwitchedTelephone Network (PSTN), other wireless networks, and any other networktopology that can be used to interconnect the information processingsystems.

In selected embodiments, the information processing systems includenonvolatile data stores, such as hard drives and/or nonvolatile memory.Some of the information processing systems may use separate nonvolatiledata stores. For example, server 162 utilizes nonvolatile data store164, and mainframe computer 158 utilizes nonvolatile data store 160. Thenonvolatile data store can be a component that is external to thevarious information processing systems or can be internal to one of theinformation processing systems. An illustrative example of aninformation processing system showing an exemplary processor and variouscomponents commonly accessed by the processor is shown in FIG. 2.

In various embodiments, the QA system 100 is implemented to receive avariety of data from various computing devices (e.g., 150, 152, 154,156, 158, 162) and data sources 166, which in turn is used to perform QAoperations described in greater detail herein. In certain embodiments,the QA system 100 may receive a first set of information from a firstcomputing device (e.g., laptop computer 154). The QA system 100 thenuses the first set of data to perform QA processing operations resultingin the generation of a second set of data, which in turn is provided toa second computing device (e.g., server 162). In response, the secondcomputing device may process the second set of data to generate a thirdset of data, which is then provided back to the QA system 100. In turn,the QA system may perform additional QA processing operations on thethird set of data to generate a fourth set of data, which is thenprovided to the first computing device.

In certain embodiments, a first computing device (e.g., server 162) mayreceive a first set of data from the QA system 100, which is thenprocessed and provided as a second set of data to another computingdevice (e.g., mainframe 158). The second set of data is processed by thesecond computing device to generate a third set of data, which isprovided back to the first computing device. The second computing devicethen processes the third set of data to generate a fourth set of data,which is then provided to the QA system 100, where it is used to performQA operations described in greater detail herein.

In one embodiment, the QA system may receive a first set of data from afirst computing device (e.g., handheld computer/mobile device 150),which is then used to perform QA operations resulting in a second set ofdata. The second set of data is then provided back to the firstcomputing device, where it is used to generate a third set of data. Inturn, the third set of data is provided back to the QA system 100, whichthen provides it to a second computing device (e.g., mainframe computer158), where it is used to perform post processing operations.

As an example, a content user 130 may ask the question, “I'm looking fora good pizza restaurant nearby.” In response, the QA system 100 mayprovide a list of three such restaurants in a half mile radius of thecontent user. In turn, the content user 130 may then select one of therecommended restaurants and ask for directions, signifying their intentto proceed to the selected restaurant. In this example, the list ofrecommended restaurants, and the restaurant the content user 130selected, would be the third set of data provided to the QA system 100.To continue the example, the QA system 100 may then provide the thirdset of data to the second computing device, where it would be processedto generate a database of the most popular restaurants, byclassification, location, and other criteria.

In various embodiments the exchange of data between various computingdevices (e.g., 150, 152, 154, 156, 158, 162) results in more efficientprocessing of data as each of the computing devices can be optimized forthe types of data it processes. Likewise, the most appropriate data fora particular purpose can be sourced from the most suitable computingdevice (e.g., 150, 152, 154, 156, 158, 162), or data source 166, therebyincreasing processing efficiency. Skilled practitioners of the art willrealize that many such embodiments are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

FIG. 2 illustrates an information processing system 202, moreparticularly, a processor and common components, which is a simplifiedexample of a computer system capable of performing the computingoperations described herein. Information processing system 202 includesa processor unit 204 that is coupled to a system bus 206. A videoadapter 208, which controls a display 210, is also coupled to system bus206. System bus 206 is coupled via a bus bridge 212 to an Input/Output(I/O) bus 214. An I/O interface 216 is coupled to I/O bus 214. The I/Ointerface 216 affords communication with various I/O devices, includinga keyboard 218, a mouse 220, a Compact Disk-Read Only Memory (CD-ROM)drive 222, a floppy disk drive 224, and a flash drive memory 226. Theformat of the ports connected to I/O interface 216 may be any known tothose skilled in the art of computer architecture, including but notlimited to Universal Serial Bus (USB) ports.

The information processing system 202 is able to communicate with aservice provider server 252 via a network 228 using a network interface230, which is coupled to system bus 206. Network 228 may be an externalnetwork such as the Internet, or an internal network such as an EthernetNetwork or a Virtual Private Network (VPN). Using network 228, clientcomputer 202 is able to use the present invention to access serviceprovider server 252.

A hard drive interface 232 is also coupled to system bus 206. Hard driveinterface 232 interfaces with a hard drive 234. In a preferredembodiment, hard drive 234 populates a system memory 236, which is alsocoupled to system bus 206. Data that populates system memory 236includes the information processing system's 202 operating system (OS)238 and software programs 244.

OS 238 includes a shell 240 for providing transparent user access toresources such as software programs 244. Generally, shell 240 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 240 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 240 (as it is called in UNIX®), also called a commandprocessor in Windows®, is generally the highest level of the operatingsystem software hierarchy and serves as a command interpreter. The shellprovides a system prompt, interprets commands entered by keyboard,mouse, or other user input media, and sends the interpreted command(s)to the appropriate lower levels of the operating system (e.g., a kernel242) for processing. While shell 240 generally is a text-based,line-oriented user interface, the present invention can also supportother user interface modes, such as graphical, voice, gestural, etc.

As depicted, OS 238 also includes kernel 242, which includes lowerlevels of functionality for OS 238, including essential servicesrequired by other parts of OS 238 and software programs 244, includingmemory management, process and task management, disk management, andmouse and keyboard management. Software programs 244 may include abrowser 246 and email client 248. Browser 246 includes program modulesand instructions enabling a World Wide Web (WWW) client (i.e.,information processing system 202) to send and receive network messagesto the Internet using HyperText Transfer Protocol (HTTP) messaging, thusenabling communication with service provider server 252. In variousembodiments, software programs 244 may also include a table-basedgroundtruth generation system 250. In these and other embodiments, thetable-based groundtruth generation system 250 includes code forimplementing the processes described hereinbelow. In one embodiment, theinformation processing system 202 is able to download the table-basedgroundtruth generation system 250 from a service provider server 252.

The hardware elements depicted in the information processing system 202are not intended to be exhaustive, but rather are representative tohighlight components used by the present invention. For instance, theinformation processing system 202 may include alternate memory storagedevices such as magnetic cassettes, Digital Versatile Disks (DVDs),Bernoulli cartridges, and the like. These and other variations areintended to be within the spirit, scope and intent of the presentinvention.

FIG. 3 is a simplified block diagram of a table-based groundtruthgeneration system implemented in accordance with an embodiment of theinvention. As used herein, groundtruth broadly refers to a set ofquestion-answer (QA) pairs used to train a machine learning system, suchas a QA system, where each question of an associated QA pair has acorresponding correct answer. As likewise used herein, supervisedlearning approaches broadly refer to various machine learning approachesfor inferring a function from labeled training data, which typicallyconsists of training examples. In these approaches, a QA system isprovided example inputs consisting of labeled training data, and theirdesired outputs, with the goal of generating a general rule that cansubsequently be used to associate a given input with a correspondingoutput.

In various embodiments, such training data is generated by domainexperts, such as a user 322, that first create questions and then linkthem to one or more correct answers. In certain embodiments, thetraining data is automatically generated, as described in greater detailherein. In certain embodiments, the resulting QA pairs are then used fortraining the QA system.

As likewise used herein, a table broadly refers to a collection ofstructured data arranged in rows and columns, the intersections of whichare commonly referred to as cells, which may or may not contain data. Invarious embodiments, each row or column associated with a table may havea corresponding label. In these embodiments, the label may be associatedwith a field, parameter, property, attribute, and so forth, which inturn may be represented by a word, phrase, or numerical index. Incertain embodiments, a row may be a record, such as a database record.

In various embodiments, a table may be contained within a corpus ofotherwise unstructured data, such as a human-readable text. In theseembodiments, various columns, rows or cells of the table may bereferenced within the unstructured data. In certain embodiments, a tablemay not be contained within a corpus of unstructured data, butreferenced therein. As an example, a text document may containreferences to various financial information stored in a table containedin a different text document, or alternatively, in a binary file such asa spreadsheet. Skilled practitioners of the art will recognize that manysuch embodiments and examples are possible. Accordingly, the foregoingis not intended to limit the spirit, scope or intent of the invention.

In various embodiments, an input corpus 302 of human-readable textincludes one or more documents 304, which in turn may containunstructured text 306, one or more tables 308, and one or more bulletlists 328. In certain embodiments, the table 308 may include column 310labels, row 312 labels, associated cells 314 containing structured data,or some combination thereof. In one embodiment, structured data within atable 308, and its corresponding column 310 and row 312 labels, isprocessed by a table-based groundtruth generation system 250 to generatea QA pair.

In another embodiment, structured or unstructured data within a bulletlist 328 is processed by the table-based groundtruth generation system250 to generate a QA pair. However, skilled practitioners of the artwill recognize that generating QA pairs from repeated-structure content,such as that commonly found in a table 308, may result in a plethora ofquestions that may not be of significant interest to a user 322.Accordingly, the resulting QA pairs generated from information withinthe table 308, or bullet list 328, are assigned a low initial rankingscore, as described in greater detail herein.

In one embodiment, unstructured text 306 containing a reference toparticular data in a table 308, the referenced data within the table308, and its corresponding column 310 and row 312 labels are processedby the table-based groundtruth generation system 250 to generate a QApair. In another embodiment, unstructured text 306 containing areference to particular data in a bullet list 328, and the referenceddata within the bullet list 328, is processed by the table-basedgroundtruth generation system 250 to generate a QA pair. In theseembodiments, the resulting QA pair is assigned a relatively high rankingscore as it would likely reference information that is of interest to auser 322.

In various embodiments, QA pairs are generated with the assistance of auser 322 interacting with a groundtruth generation system. In oneembodiment, the user 322 references data within the table 308 togenerate the QA pairs. In another embodiment, the user 322 referencesdata within the bullet list 328 to generate the QA pairs. In variousembodiments, the QA pairs are generated by a user 322 interacting withtable-based groundtruth generation system 250. In certain of theseembodiments, the table-based groundtruth generation system 250 may allowthe user to enter a question and subsequently present a series ofpotential answers for the user 322 to use in the generation of a QApair. In certain embodiments, when a user chooses a correct answer thatis in a table or bullet list, a first QA pair is generated, where thequestion is the user's entered question and the answer is the user'schosen answer. In certain embodiments when a user chooses a correctanswer that is in a table or bullet list, the groundtruth generationsystem may then automatically create additional QA pairs using theuser-generated QA pair as a template, and applying that template to thetable or bulleted list. The method by which the table-based groundtruthgeneration system 250 provides the questions, answers, or a combinationthereof, to the user 322 and subsequently creates additional QA pairs isa matter of design choice.

Once these various QA pairs are generated by the table-based groundtruthgeneration system 250, they are assigned an associated ranking score, asdescribed in greater detail herein, and then stored in a repository ofQA pairs 320. In various embodiments, these QA pairs are then rankedaccording to their respective ranking scores. In certain embodiments, QApairs generated by or with the assistance of the user 322 are used inranking operations to determine the ranking of the QA pairs. In variousembodiments, QA pairs generated by or with the assistance of the user322 are assigned a particular ranking score, as likewise described ingreater detail herein, to indicate their respective significance. Incertain embodiments, the repository of QA pairs 320 may include arepository of user questions and/or QA pairs that aren't generated bythe groundtruth-generation system 250. In certain embodiments, the userquestions and/or QA pairs that are not generated by the groundtruthgeneration system 250 can be used to affect QA pair scores. Once ranked,the QA pairs are used as a groundtruth training set in supervisedlearning approaches to train a QA system. For example, the highestscored QA pairs, up to a desired number, can be chosen to train thesystem. Those of skill in the art will recognize that many suchembodiments are possible. Accordingly the foregoing is not intended tolimit the spirit, scope or intent of the invention.

Referring now to FIG. 3, table-based groundtruth generation operationsare initiated in this embodiment by the receipt of an input corpus 302of human-readable text, such as a collection of documents 304, by atable-based groundtruth generation system 250. A target document 304within the input corpus 302 is selected, followed by the identificationof any tables 308 it may contain. A target table 308 is selected andthen processed to parse its associated column 310 and row 312 labels.

In various embodiments, the parsing is performed by a table parser 318.In certain embodiments, the table-based groundtruth generation system250 includes the table parser 318. In one embodiment, the table parser318 is an independent system. In another embodiment, the table parser318 is part of another system, such as a QA system, described in greaterdetail herein. In yet another embodiment, the functionality of the tableparser 318 is provided as a service delivered over a network connection.Skilled practitioners of the art will recognize that many suchembodiments are possible. Accordingly, the foregoing is not intended tolimit the spirit, scope or intent of the invention.

The type of content (e.g., numerical, categorical, percentage, date,time, etc.) associated with the column 310 and row 312 labels is thenidentified and assigned as metadata to each cell 314 within the table308 (and to the corresponding column 310 and row 312 labels). In certainembodiments, the type of content associated with the column 310 and row312 labels (and assigned as metadata) can also include keyword andentity information. Entries in the cells can be used to aididentification of the type of content. An entity is a category, such as“city.” “New York” is a keyword and a specific example of the cityentity. Questions for QA pairs are then automatically generated byapplying direct statement templates to the contents of the table 308. Asused herein, a direct statement broadly refers to a statement containingdata that is directly stated within a document 304. In certainembodiments, the directly-stated data is contained in a table 308.

In various embodiments, use of the direct statement templateautomatically generates a question for a QA pair by combining words orphrases such as “who,” “what,” “when,” “where,” “how many,” “whatpercentage,” and so forth, with information contained in column 310 androw 312 labels. In certain embodiments, answers for the QA pairs arecontained within one or more cells 314 of the table 308 respectivelyassociated with their corresponding column 310 and row 312 labels. Inthese embodiments, a low initial ranking score (e.g., score=0.1) isassigned to each of the resulting QA pairs to signify their respectivesignificance, as they are generated from direct statements associatedwith the contents of the table 308 and may not necessarily reflecttypical end-user questions. The method by which the initial score isselected and assigned is a matter of design choice.

Unstructured text 306 in the selected document 304 is then processed toidentify references to the column 310 and row 312 labels of the table308. The initial ranking scores of the QA pairs previously generatedthrough the use of direct templates, that are likewise associated withone or more of the identified references to column 310 and row 312labels of the table 308, are then increased. In various embodiments, theranking scores are increased by a greater amount if the references tothe column 310 and row 312 labels in the unstructured text 306 areproximate to one another, such as in the same sentence 316. In theseembodiments, the amount by which these ranking scores are increased is amatter of design choice.

Unstructured text 306 in the selected document 304 is then furtherprocessed to extract portions 316, such as a sentence, containingreferences to tables, such as direct references to tables (e.g., “asseen in Table 1, . . . ”), to cells 314 (e.g., amounts, percentages,etc.) or column 310 or row 312 labels, such as groups, classifications,characteristics, keywords, entities and so forth. An extracted portion316 of the unstructured text 306 is then selected, followed by adetermination being made whether one or more QA pairs exist for theselected portion 316. If so, then they are referenced to the selectedportion 316 of unstructured text 306. Otherwise, QA generationoperations familiar to those of skill in the art are performed on theselected portion 316 of unstructured text 306 to generate one or more QApairs.

The previously referenced or generated QA pairs, each of which contain aquestion whose answer is in the selected portion 316 of the unstructuredtext 306, are then assigned a high initial ranking score (e.g.,score=1), as described in greater detail herein. These questions arethen processed to extract any associated entities and keywords. In turn,the extracted entities and keywords are respectively mapped to theircorresponding column 310 and row 312 labels. In various embodiments, themapping operations are implemented to accommodate non-exact mapping ofentities and keywords. For example, abbreviations of column 310 labels,row 312 labels, entities, keywords, or any combination thereof, may bemapped to one another. The method by which the mapping operations areimplemented is a matter of design choice.

A QA pair generated from the referenced or generated QA pairs is thenselected, followed by the selection of a question associated with theselected QA pair. In various embodiments, some or all identifiedentities and keywords in the selected question (e.g. question ‘q’) arethen replaced with wildcards to generate a question template (e.g.,TEMPLATE_(q)). The resulting question template is then applied to thetable to create new questions by replacing the wildcards it containswith entities and keywords similar to those previously extracted. As anexample, the entity “Male” may be replaced with a wildcard ‘M’, which inturn may be replaced with the word “Men.” The method of determiningwhich entities and keywords are similar to those replaced is a matter ofdesign choice. Skilled practitioners of the art will recognize that manysuch examples and methods are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope or intent of the invention. Theprocess is continued, question by question, QA pair by QA pair, untilgeneration of additional QA pairs is concluded. The method by which itis decided to conclude generation of QA pairs is a matter of designchoice. In an embodiment, the QA pairs generated using the createdtemplates are assigned a lower initial score (e.g., score=0.5) than theQA pairs referenced to or generated from the extracted unstructuredtext.

Skilled practitioners of the art will recognize that in certainembodiments the assignment of higher ranking scores may be based upon ascore generation methodology. In certain embodiments the scoregeneration methodology may be based on one or more factors. Morespecifically, with a first score generation methodology the unstructuredtext 306 associated with a given QA pair may reference particular datacontained in the table 308. Accordingly, referenced data contained inthe table 308 is typically more relevant than that which is notreferenced in the unstructured text 306 and thus should have anassociated higher score. With a second score generation methodology, aQA pair based upon the unstructured text 306 is typically of moreinterest to a user than a QA pair whose associated groundtruth is solelybased upon the contents of the table 308 and thus should have anassociated higher score.

The scored QA pairs are then stored in a repository of QA pairs 320,followed by a determination being made whether to select another portion316 of unstructured text 306. If so, the process is repeated, asdescribed, until it is decided not to select another portion 316 ofunstructured text 306. A determination is then made whether to selectanother table 308 within the selected document 304. If so, then theprocess is repeated, as described, until it is decided not to selectanother table 308 within the selected document 304.

Once all portions of unstructured text 316, tables 308, and documents304 have been selected and processed as described, the QA pairs storedin the repository of QA pairs 320 are ranked according to theirrespective ranking scores. The ranked QA pairs are then used to train aQA system, as described in greater detail herein.

FIG. 4 is an exemplary table used in accordance with an embodiment ofthe invention for automating the generation of table-based groundtruth.In this embodiment, a document containing an exemplary table 400 isreceived by a table-based groundtruth generation system. Parsingoperations are performed on the document to extract the table 400,followed by performing additional parsing operations on the extractedtable 400 to parse its associated column 410 and row 412 labels. Thetype of content (e.g., numerical, categorical, percentage, date, time,keywords, entities, etc.) associated with the column 410 and row 412labels is then identified and assigned as metadata to each cell 414within the table 400 and to the column 410 and row 412 labels.

Questions for QA pairs are then automatically generated by applyingdirect statement templates, as described in greater detail herein, tothe contents of the table 400. In various embodiments, use of the directstatement template automatically generates a question for a QA pair bycombining words or phrases such as “who,” “what,” “when,” “where,” “howmany,” “what percentage,” and so forth, with information contained incolumn 410 and row 412 labels. For example, based upon the contents ofthe table 400 shown in FIG. 4, such questions may include:

-   -   “What is the mean Age of subjects in the Control (C) group?”    -   “What is the mean Age of subjects in the Psoriatic (P) group?”    -   “How many Male subjects are in the Control (C) group?”    -   “How many Male subjects are in the Psoriatic (P) group?”    -   “How many White subjects are in the Mild Psoriasis (MP) group?”    -   “How many Hispanic subjects are in the Severe Psoriasis (SP)        group?”

In certain embodiments, answers for the QA pairs are contained withinone or more cells 414 of the table 400 respectively associated withtheir corresponding column 410 and row 412 labels. To continue thepreceding example, such answers may include:

-   -   “The mean Age of subjects in the Control (C) group is 11.5.”    -   “The mean Age of subjects in the Psoriatic (P) group is 12.2.”    -   “There are 96 Male subjects are in the Control (C) group.”    -   “There are 178 Male subjects are in the Psoriatic (P) group.”    -   “There are 134 White subjects are in the Mild Psoriasis (MP)        group.”    -   “There are 26 Hispanic subjects are in the Severe Psoriasis (SP)        group.”

A low initial ranking score (e.g., score=0.1) is then assigned to eachof the resulting QA pairs to signify their respective significance, asthey are generated from direct statements associated with the contentsof the table 400 and may not necessarily reflect typical end-userquestions. Unstructured text within the document associated with thetable 400 is then processed to identify references to its column 410 androw 412 labels. The initial ranking scores of the QA pairs previouslygenerated through the use of direct templates, that are likewiseassociated with one or more of the identified references to column 410and row 412 labels of the table 400, are then increased as described ingreater detail. For example, in an embodiment a sentence in theunstructured text, “Mild Psoriasis (MP) sufferers are frequentlymisdiagnosed” would cause the score for the QA pair including thequestion “How many White subjects are in the Mild Psoriasis (MP) group?”to have its score increased, since that QA pair is associated with acolumn label including “MP.” As another example, “Hispanic” and “White”could be recognized as examples of the entity “ethnicity.” The metadataassociated with the row 412 labels containing “Hispanic” and “White” inthe table 400 could include entity ethnicity. In an embodiment,unstructured text stating “Ethnicity is studied” could then cause thequestions “How many White subjects are in the Mild Psoriasis (MP)group?” and “How many Hispanic subjects are in the Severe Psoriasis (SP)group?” to have their scores increased based on their associated rowlabels being recognized as examples of the entity ethnicity.

Unstructured text within the document is then further processed toextract portions, such as a sentence, that contain references to thecontents of the table 400. Continuing the preceding example, a portionof the unstructured text (not shown) may state:

-   -   “More SP than MP children had family history of obesity (24.7%        of MP and 33.9% of SP)”

An extracted portion of the unstructured text is then selected, followedby a determination being made whether one or more QA pairs exists forit, e.g., in a database of QA pairs, which may have been created bylogging user queries during use of this or a similar QA system orcreated by a subject matter expert. If so, then they are referenced tothe selected portion of unstructured text. Otherwise, QA generationoperations familiar to those of skill in the art are performed on theselected portion of unstructured text to generate one or more QA pairs,each of which contains a question whose answer is in the selectedportion of the unstructured text. In continuance of the precedingexample:

-   -   Question: “How does family history of obesity vary in SP versus        MP children?”    -   Answer: “More SP (33.9%) than MP (24.7%) children have family        history of obesity.”

A high initial ranking score is assigned (e.g, score=1). The highranking score reflects the fact that information called out in theunstructured text is likely to be of interest to users. The resultingquestions are then processed to extract any associated entities andkeywords, which are in turn respectively mapped to their correspondingcolumn 410 and row 412 labels and templates are created which are usedto automatically generate questions. For example, C, P, MP and SP arerecognized as keywords corresponding to the entity population and“family history of obesity” is recognized as a keyword. A template “Howdoes ‘family history of obesity’ vary in <population1> versus<population2>” is created and, the following additional QA pair can beautomatically generated from the contents of the table 400 shown in FIG.4 (it will be appreciated that answers may be simple answers from one ormore cells or answers generated from an answer template):

-   -   Question: “How does family history of obesity vary in Control        children versus Psoriatic?”    -   Answer: “More Control (30.4%) than Psoriatic (29.6%) children        have family history of obesity.

In various embodiments, the mapping operations are implemented toaccommodate non-exact mapping of entities and keywords. For example,abbreviations of column 410 labels, row 412 labels, entities, keywords,or any combination thereof, may be mapped to one another. A lowerinitial ranking score (e.g., score=0.5) is then assigned to each QA pairgenerated using a template that is based on a question referenced to orcreated from the extracted portion of unstructured text. Skilledpractitioners of the art will recognize that many such examples, methodsand embodiments are possible. Accordingly, the foregoing is not intendedto limit the spirit, scope or intent of the invention.

FIGS. 5a through 5c are a generalized flowchart of the performance oftable-based groundtruth generation operations implemented in accordancewith an embodiment of the invention. In this embodiment, table-basedgroundtruth generation operations are begun in step 502, followed by thereceipt of a corpus of human-readable text, such as a collection ofdocuments, in step 504. A target document within the corpus is selectedin step 506, followed by the identification of any tables it may containin step 508. A determination is then made in step 510 whether thedocument contains one or more tables.

If so, then the document is processed in step 512 to parse any tables itmay contain, followed by the selection of a target table in step 514.The selected table is then processed in step 516 to parse its associatedcolumn and row labels. In turn, the type of content (e.g., numerical,categorical, percentage, date, time, keywords, entities, etc.)associated with each of the parsed column and row labels, individuallyor in combination, is identified in step 518 and then assigned in step520 as metadata to each cell within the table.

Questions for question-answer (QA) pairs are then automaticallygenerated in step 522 by applying direct statement templates to thecontents of the table. The resulting questions, the row/column labeldata associated with the table, and the contents of the table's cellsare then processed in step 524 to generate associated QA pairs. A lowinitial score (e.g., score=0.1) is then assigned in step 526 to each ofthe resulting QA pairs to signify their respective significance, as theyare generated from direct statements associated with the contents of thetable and may not necessarily reflect typical end-user questions.

Unstructured text in the selected document is then processed in step 528to identify references to the table's column and row labels. The rankingscores of QA pairs generated in step 524, that are likewise associatedwith one or more of the identified references to the table's column androw labels, are then increased in step 530, as described in greaterdetail herein. In various embodiments, the scores are increased by agreater amount if the references to the column and row labels in theunstructured text are proximate to one another, such as in the samesentence.

Unstructured text in the selected document is then further processed instep 532 to extract portions that contain references to a table, such asthe table 400 shown in FIG. 4. In certain embodiments, the references toa table may include the contents of individual cells, column labels, rowlabels, associated metadata, or any combination thereof. An extractedportion of the unstructured text is then selected in step 534, followedby a determination being made in step 536 whether one or more QA pairsexist for the selected portion. If so, then they are referenced to theselected portion of unstructured text in step 538. Otherwise, QAgeneration operations familiar to those of skill in the art areperformed in step 540 on the selected portion of unstructured text, thecontents of the cell it references, and the cell's associated column androw labels, to generate one or more QA pairs. In various embodiments,the selected portion of unstructured text contains the answer associatedwith certain of the QA pairs referenced in step 538 or generated in step540.

The QA pairs referenced in step 538, or generated in step 540, are thenassigned a ranking score in step 542, as described in greater detailherein, according to indicators of user interest. In turn, the QA pairsreferenced in step 538, or generated in step 540 are then processed instep 544 to extract any associated entities and keywords. The extractedentities and keywords are then respectively mapped to theircorresponding column and row labels in step 546.

Then, in step 548, a QA pair referenced in step 538, or generated instep 540, is selected, followed by replacing identified entities andkeywords in the selected QA pair with wildcards in step 550 to generatea template for the selected QA pair. The resulting template is then usedin step 552 to create new QA pairs by replacing the wildcards itcontains with entities and keywords similar to those extracted in step544. The QA pairs resulting are then assigned a ranking score in step556, as described in greater detail herein.

A determination is then made in step 558 whether to select another QApair. If so, then the process is continued, proceeding with step 548. Ifnot, then, in step 562, the scored QA pairs are stored in a repositoryof QA pairs, followed by a determination being made in step 564 whetherto select another portion of unstructured text. If so, the process iscontinued, proceeding with step 534. Otherwise, a determination is madein step 566 whether to select another table. If so, then the process iscontinued, proceeding with step 514.

Otherwise, or if it was determined in step 510 that the selecteddocument does not contain one or more tables, then a determination ismade in step 568 whether to select another document in the corpus. Ifso, the process is continued, proceeding with step 506. Otherwise, theQA pairs stored in the repository of QA pairs are ranked in step 570according to their respective ranking scores. The ranked QA pairs arethen used in step 572 to train a QA system, as described in greaterdetail herein, and table-based groundtruth generation operations areended in step 574.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

For example, in various embodiments, the ranking score assigned to agiven QA pair may be adjusted to reflect its significance to a user 322of the QA system being trained. For example, in certain embodiments, theranking score is increased by a first amount (e.g., +2) if a given term(e.g., “psoriasis”) in the unstructured text 306 is used in either thequestion or answer of a corresponding QA pair. In another embodiment,the ranking score is increased by a second amount (e.g., +1) if anassociated concept (e.g., “abnormal skin condition”) in the unstructuredtext 306 is referenced in either the question or answer of acorresponding QA pair. For example, if the QA pair contains the term“psoriasis” and the unstructured text contains the term “abnormal skincondition”, the QA pair's score would be increased by this amount. Ifthere are multiple instances in the unstructured text of a given term orconcept used or referenced in a QA pair, the QA pair's score can beincreased for just the first occurrence or repeatedly for multipleoccurrences. In various embodiments, the scoring strategies can beapplied to table-based questions that are generated in any way, e.g.,generating strategies as disclosed herein and/or using known generatingstrategies. In certain embodiments, the scoring strategies may also beapplied to questions or QA pairs generated by a user who does not knowwell what users of the QA system being trained will be likely to ask.

In yet another embodiment, the ranking score is increased by anotheramount (e.g., +existing_score^proximity_measure) if two or more giventerms (e.g., “psoriasis” and “dermatitis”) appear in the unstructuredtext and are used in either the question or answer of a corresponding QApair. In this embodiment, the value of the proximity_measure is ameasure of how proximate the given terms are to each other in theunstructured text, such as in the same sentence 316. The method by whichthe value of the proximity_measure is determined in this embodiment is amatter of design choice. In yet still another embodiment, the rankingscore is increased by another amount (e.g., +3) if a given term (e.g.,“psoriasis”), or an associated concept (e.g., “skin disorder”), in theQA pair is referenced in a query submitted by a user 322 of the QAsystem. In certain embodiments, queries submitted by a user of the QAsystem may be stored in a database of user queries that may have beencollected, during use of the QA system or a similar QA system.

In one embodiment, the ranking score is increased by another amount(e.g., +existing_score*8) if the corresponding QA pair is derived fromunstructured text 306 that references the table 308, one or more column310 labels, one or more row 312 labels, one or more table cells 314, orany combination thereof. In certain embodiments, if the question hasalready been assigned a high score, the ranking score would notnecessary be increased. In another embodiment, the ranking score isincreased by another amount (e.g., +existing_score*10) if the questionassociated with corresponding QA pair contains a query, or portionthereof, submitted by a user 322. In yet another embodiment, the rankingscore is increased by another amount (e.g., +existing_score*5) if thequestion associated with a corresponding QA pair is derived from aquestion submitted by a user 322 of the QA system. In certainembodiments, queries submitted by a user of the QA system may be storedin a database of user queries that may have been collected, during useof the QA system or a similar QA system.

In yet still another embodiment, the ranking score is increased byanother amount (e.g., +2) if the question refers to a relationship(e.g., population ‘x’ has a larger incidence than population ‘y’) thatis detected in the unstructured text 306 that references the table 308,one or more column 310 labels, one or more row 312 labels, one or moretable cells 314, or any combination thereof. In certain embodiments, theassignment of higher ranking scores can be based on one or more of aplurality of factors. For example, the factors may take intoconsideration data in a QA pair that is referenced in a user query asthis data is typically more relevant than data that is not. Also forexample data that is referenced in a question a user inputs to agroundtruth generation system is typically more relevant that data thatis not. Also, factors may take into consideration subtleties such aswhen unstructured text references are in close proximity to one anotherare more indicative of user interest than distant ones, etc. In theseembodiments, the amount by which a ranking score is adjusted, and themethod by which the amount is determined, is a matter of design choice.

Those of skill in the art will recognize that many such embodiments arepossible. Accordingly, the foregoing is not intended to limit thespirit, scope or intent of the invention.

What is claimed is:
 1. A computer-implemented method for automating thegeneration of table-based groundtruth, comprising: receiving a documentcomprising unstructured text and a table, the table comprisingquantitative information in a tabular format, the table comprisingrepeated-structure content; generating questions by applying a templateto the contents of the table, the template comprising a direct statementtemplate, the direct statement template comprising a statementcontaining data that is directly stated within the document; performingQA pair generation operations on the table to generate QA pairs, each QApair comprising a question generated by applying the template; and,assigning a score to each QA pair, the score providing an indicator ofuser interest to each QA pair, the score being based on a scoregeneration methodology using the unstructured text and the table, thescore for each QA pair comprising a ranking score for each QA pair;identifying groundtruth QA pairs based upon the ranking score of each QApair; and, training a QA system using the groundtruth QA pairs.
 2. Themethod of claim 1, further comprising: parsing the table to generatelabel data associated with its columns and rows; associating label datawith a cell corresponding to a column and a row of the table;determining whether the unstructured text includes a reference to thelabel data; and, increasing the score for a QA pair associated with thelabel data.
 3. The method of claim 2, further comprising: determiningwhether the unstructured text includes another reference to the labeldata; and, further increasing the score for the QA pair associated withthe label data if the reference and the another reference are proximateto one another.
 4. The method of claim 1, further comprising: parsingthe unstructured text in the document; determining whether a parsedportion of the unstructured text references content within the table;generating a first QA pair relating to the unstructured text referencingcontent within the table; processing the first QA pair to identifyentities and keywords; creating a question template based on theentities and keywords; using the question template to create a questionfor a second QA pair based upon the entities and keywords; and,assigning scores to each of the QA pairs.
 5. The method of claim 4,further comprising: assigning a higher score to a QA pair when aquestion of the QA pair is generated from the unstructured text.
 6. Themethod of claim 5, further comprising: assigning a lower score to a QApair when a question of the QA pair is generated using the questiontemplate created based on the entities and keywords.
 7. A systemcomprising: a processor; a data bus coupled to the processor; and acomputer-usable medium embodying computer program code, thecomputer-usable medium being coupled to the data bus, the computerprogram code used for automating the generation of table-basedgroundtruths and comprising instructions executable by the processor andconfigured for: receiving a document comprising unstructured text and atable, the table comprising quantitative information in a tabularformat, the table comprising repeated-structure content; generatingquestions by applying a template to the contents of the table, thetemplate comprising a direct statement template, the direct statementtemplate comprising a statement containing data that is directly statedwithin the document; performing QA pair generation operations on thetable to generate QA pairs, each QA pair comprising a question generatedby applying the template; and, assigning a score to each QA pair, thescore providing an indicator of user interest to each QA pair, the scorebeing based on a score generation methodology using the unstructuredtext and the table, the score for each QA pair comprising a rankingscore for each QA pair; identifying groundtruth QA pairs based upon theranking score of each QA pair; and training a QA system using thegroundtruth QA pairs.
 8. The system of claim 7, wherein the instructionsare further configured for: parsing the table to generate label dataassociated with its columns and rows; associating label data with a cellcorresponding to a column and a row of the table; determining whetherthe unstructured text includes a reference to the label data; and,increasing the score for a QA pair associated with the label data. 9.The system of claim 8, wherein the instructions are further configuredfor: determining whether the unstructured text includes anotherreference to the label data; and, further increasing the score for theQA pair associated with the label data if the reference and the anotherreference are proximate to one another.
 10. The system of claim 7,wherein the instructions are further configured for: parsing theunstructured text in the document; determining whether a parse portionof the unstructured text references content within the table; generatinga first QA pair relating to the unstructured text referencing contentwithin the table; processing the first QA pair to identify entities andkeywords; creating a question template based on the entities andkeywords; using the question template to create a question for a secondQA pair based upon the entities and keywords; and, assigning a score tothe question for each of the QA pairs.
 11. The system of claim 10,wherein the instructions are further configured for: assigning a higherscore to a QA pair when a question of the QA pair is generated from theunstructured text.
 12. The system of claim 11, wherein the instructionsare further configured for: assigning a lower score to a QA pair when aquestion of the QA pair is generated using the question template createdbased on the entities and keywords.
 13. A non-transitory,computer-readable storage medium embodying computer program code, thecomputer program code comprising computer executable instructionsconfigured for: receiving a document comprising unstructured text and atable, the table comprising quantitative information in a tabularformat, the table comprising repeated-structure content; generatingquestions by applying a template to the contents of the table, thetemplate comprising a direct statement template, the direct statementtemplate comprising a statement containing data that is directly statedwithin the document; performing QA pair generation operations on thetable to generate QA pairs, each QA pair comprising a question generatedby applying the template; and, assigning a score to each QA pair, thescore providing an indicator of user interest to each QA pair, the scorebeing based on a score generation methodology using the unstructuredtext and the table, the score for each QA pair comprising a rankingscore for each QA pair; identifying groundtruth QA pairs based upon theranking score of each QA pair; and, training a QA system using thegroundtruth QA pairs.
 14. The computer-readable storage medium of claim13, wherein the instructions are further configured for: parsing thetable to generate label data associated with its columns and rows;associating label data with a cell corresponding to a column and a rowof the table; determining whether the unstructured text includes areference to the label data; and, increasing the score for a QA pairassociated with the label data.
 15. The computer-readable storage mediumof claim 14, wherein the instructions are further configured for:determining whether the unstructured text includes another reference tothe label data; and, further increasing the score for the QA pairassociated with the label data if the reference and the anotherreference are proximate to one another.
 16. The computer-readablestorage medium of claim 13, wherein the instructions are furtherconfigured for: parsing the unstructured text in the document;determining whether a parse portion of the unstructured text referencescontent within the table; generating a first QA pair relating to theunstructured text referencing content within the table; processing thefirst QA pair to identify entities and keywords; creating a questiontemplate based on the entities and keywords; using the question templateto create a question for a second QA pair based upon the entities andkeywords; and, assigning a score to the question for each of the QApairs.
 17. The computer-readable storage medium of claim 16, wherein theinstructions are further configured for: assigning a higher score to aQA pair when a question of the QA pair is generated from theunstructured text.
 18. The computer-readable storage medium of claim 17,wherein the instructions are further configured for: assigning a lowerscore to a QA pair when a question of the QA pair is generated using thequestion template created based on the entities and keywords.
 19. Thenon-transitory, computer-readable storage medium of claim 13, whereinthe computer executable instructions are deployable to a client systemfrom a server system at a remote location.
 20. The non-transitory,computer-readable storage medium of claim 13, wherein the computerexecutable instructions are provided by a service provider to a user onan on-demand basis.